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market_basket <- list(
  c("apple","beer","rice","meat"),
  c("apple","beer","rice"),
  c("apple","pear"),
  c("apple","beer"),
  c("milk","beer","rice","meat"),
  c("milk","beer","rice"),
  c("milk","beer"),
  c("milk","pear")
)

names(market_basket) <- paste("T -",c(1:8),sep = " ")

print(market_basket)

$`T - 1`
[1] "apple" "beer"  "rice"  "meat" 

$`T - 2`
[1] "apple" "beer"  "rice" 

$`T - 3`
[1] "apple" "pear" 

$`T - 4`
[1] "apple" "beer" 

$`T - 5`
[1] "milk" "beer" "rice" "meat"

$`T - 6`
[1] "milk" "beer" "rice"

$`T - 7`
[1] "milk" "beer"

$`T - 8`
[1] "milk" "pear"

install.packages("arules")

WARNING: Rtools is required to build R packages but is not currently installed. Please download and install the appropriate version of Rtools before proceeding:

https://cran.rstudio.com/bin/windows/Rtools/
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library(arules)

Warning: package ‘arules’ was built under R version 4.2.1
Loading required package: Matrix

Attaching package: ‘arules’

The following objects are masked from ‘package:base’:

    abbreviate, write

install.packages("arulesViz")

WARNING: Rtools is required to build R packages but is not currently installed. Please download and install the appropriate version of Rtools before proceeding:

https://cran.rstudio.com/bin/windows/Rtools/
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library(arulesViz)

Warning: package ‘arulesViz’ was built under R version 4.2.1

dim(market_basket)

NULL

trans <- as(market_basket,"transactions")
dim(trans)

[1] 8 6

itemLabels(trans)

[1] "apple" "beer"  "meat"  "milk"  "pear"  "rice" 

summary(trans)

transactions as itemMatrix in sparse format with
 8 rows (elements/itemsets/transactions) and
 6 columns (items) and a density of 0.4583333 

most frequent items:
   beer   apple    milk    rice    meat (Other) 
      6       4       4       4       2       2 

element (itemset/transaction) length distribution:
sizes
2 3 4 
4 2 2 

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   2.00    2.00    2.50    2.75    3.25    4.00 

includes extended item information - examples:

includes extended transaction information - examples:

rules <- apriori(trans,parameter = list(supp = 0.3,conf = 0.5,maxlen = 10,target = "rules"))

Apriori

Parameter specification:

Algorithmic control:

Absolute minimum support count: 2 

set item appearances ...[0 item(s)] done [0.00s].
set transactions ...[6 item(s), 8 transaction(s)] done [0.00s].
sorting and recoding items ... [4 item(s)] done [0.00s].
creating transaction tree ... done [0.00s].
checking subsets of size 1 2 done [0.00s].
writing ... [10 rule(s)] done [0.00s].
creating S4 object  ... done [0.00s].

summary(rules)

set of 10 rules

rule length distribution (lhs + rhs):sizes
1 2 
4 6 

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    1.0     1.0     2.0     1.6     2.0     2.0 

summary of quality measures:
    support        confidence        coverage           lift      
 Min.   :0.375   Min.   :0.5000   Min.   :0.5000   Min.   :1.000  
 1st Qu.:0.375   1st Qu.:0.5000   1st Qu.:0.5625   1st Qu.:1.000  
 Median :0.500   Median :0.5833   Median :0.7500   Median :1.000  
 Mean   :0.475   Mean   :0.6417   Mean   :0.7750   Mean   :1.067  
 3rd Qu.:0.500   3rd Qu.:0.7500   3rd Qu.:1.0000   3rd Qu.:1.000  
 Max.   :0.750   Max.   :1.0000   Max.   :1.0000   Max.   :1.333  
     count    
 Min.   :3.0  
 1st Qu.:3.0  
 Median :4.0  
 Mean   :3.8  
 3rd Qu.:4.0  
 Max.   :6.0  

mining info:

beer_ass_rule <- apriori(trans,
                         parameter = list(supp = 0.3,conf = 0.5,maxlen = 10,minlen = 2),appearance = list(default = "rhs",lhs = "beer"))

Apriori

Parameter specification:

Algorithmic control:

Absolute minimum support count: 2 

set item appearances ...[1 item(s)] done [0.00s].
set transactions ...[6 item(s), 8 transaction(s)] done [0.00s].
sorting and recoding items ... [4 item(s)] done [0.00s].
creating transaction tree ... done [0.00s].
checking subsets of size 1 2 done [0.00s].
writing ... [3 rule(s)] done [0.00s].
creating S4 object  ... done [0.00s].

inspect(beer_ass_rule)

plot(rules,engine = "plotly")

To reduce overplotting, jitter is added! Use jitter = 0 to prevent jitter.
Registered S3 method overwritten by 'data.table':
  method           from
  print.data.table     
Registered S3 method overwritten by 'htmlwidgets':
  method           from         
  print.htmlwidget tools:rstudio

subrules <- head(rules,n  =10,by = "confidence")

data(Groceries)
class(Groceries)

[1] "transactions"
attr(,"package")
[1] "arules"

dim(Groceries)

[1] 9835  169

print(Groceries)

transactions in sparse format with
 9835 transactions (rows) and
 169 items (columns)

itemLabels(Groceries)

  [1] "frankfurter"               "sausage"                   "liver loaf"               
  [4] "ham"                       "meat"                      "finished products"        
  [7] "organic sausage"           "chicken"                   "turkey"                   
 [10] "pork"                      "beef"                      "hamburger meat"           
 [13] "fish"                      "citrus fruit"              "tropical fruit"           
 [16] "pip fruit"                 "grapes"                    "berries"                  
 [19] "nuts/prunes"               "root vegetables"           "onions"                   
 [22] "herbs"                     "other vegetables"          "packaged fruit/vegetables"
 [25] "whole milk"                "butter"                    "curd"                     
 [28] "dessert"                   "butter milk"               "yogurt"                   
 [31] "whipped/sour cream"        "beverages"                 "UHT-milk"                 
 [34] "condensed milk"            "cream"                     "soft cheese"              
 [37] "sliced cheese"             "hard cheese"               "cream cheese "            
 [40] "processed cheese"          "spread cheese"             "curd cheese"              
 [43] "specialty cheese"          "mayonnaise"                "salad dressing"           
 [46] "tidbits"                   "frozen vegetables"         "frozen fruits"            
 [49] "frozen meals"              "frozen fish"               "frozen chicken"           
 [52] "ice cream"                 "frozen dessert"            "frozen potato products"   
 [55] "domestic eggs"             "rolls/buns"                "white bread"              
 [58] "brown bread"               "pastry"                    "roll products "           
 [61] "semi-finished bread"       "zwieback"                  "potato products"          
 [64] "flour"                     "salt"                      "rice"                     
 [67] "pasta"                     "vinegar"                   "oil"                      
 [70] "margarine"                 "specialty fat"             "sugar"                    
 [73] "artif. sweetener"          "honey"                     "mustard"                  
 [76] "ketchup"                   "spices"                    "soups"                    
 [79] "ready soups"               "Instant food products"     "sauces"                   
 [82] "cereals"                   "organic products"          "baking powder"            
 [85] "preservation products"     "pudding powder"            "canned vegetables"        
 [88] "canned fruit"              "pickled vegetables"        "specialty vegetables"     
 [91] "jam"                       "sweet spreads"             "meat spreads"             
 [94] "canned fish"               "dog food"                  "cat food"                 
 [97] "pet care"                  "baby food"                 "coffee"                   
[100] "instant coffee"            "tea"                       "cocoa drinks"             
[103] "bottled water"             "soda"                      "misc. beverages"          
[106] "fruit/vegetable juice"     "syrup"                     "bottled beer"             
[109] "canned beer"               "brandy"                    "whisky"                   
[112] "liquor"                    "rum"                       "liqueur"                  
[115] "liquor (appetizer)"        "white wine"                "red/blush wine"           
[118] "prosecco"                  "sparkling wine"            "salty snack"              
[121] "popcorn"                   "nut snack"                 "snack products"           
[124] "long life bakery product"  "waffles"                   "cake bar"                 
[127] "chewing gum"               "chocolate"                 "cooking chocolate"        
[130] "specialty chocolate"       "specialty bar"             "chocolate marshmallow"    
[133] "candy"                     "seasonal products"         "detergent"                
[136] "softener"                  "decalcifier"               "dish cleaner"             
[139] "abrasive cleaner"          "cleaner"                   "toilet cleaner"           
[142] "bathroom cleaner"          "hair spray"                "dental care"              
[145] "male cosmetics"            "make up remover"           "skin care"                
[148] "female sanitary products"  "baby cosmetics"            "soap"                     
[151] "rubbing alcohol"           "hygiene articles"          "napkins"                  
[154] "dishes"                    "cookware"                  "kitchen utensil"          
[157] "cling film/bags"           "kitchen towels"            "house keeping products"   
[160] "candles"                   "light bulbs"               "sound storage medium"     
[163] "newspapers"                "photo/film"                "pot plants"               
[166] "flower soil/fertilizer"    "flower (seeds)"            "shopping bags"            
[169] "bags"                     

rule <- apriori(Groceries,parameter = list(supp = 0.01,conf = 0.5,minlen = 2,target = "rule"))

Apriori

Parameter specification:

Algorithmic control:

Absolute minimum support count: 98 

set item appearances ...[0 item(s)] done [0.00s].
set transactions ...[169 item(s), 9835 transaction(s)] done [0.01s].
sorting and recoding items ... [88 item(s)] done [0.00s].
creating transaction tree ... done [0.00s].
checking subsets of size 1 2 3 4 done [0.01s].
writing ... [15 rule(s)] done [0.00s].
creating S4 object  ... done [0.00s].

summary(rule)

set of 15 rules

rule length distribution (lhs + rhs):sizes
 3 
15 

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
      3       3       3       3       3       3 

summary of quality measures:
    support          confidence        coverage            lift           count      
 Min.   :0.01007   Min.   :0.5000   Min.   :0.01729   Min.   :1.984   Min.   : 99.0  
 1st Qu.:0.01174   1st Qu.:0.5151   1st Qu.:0.02089   1st Qu.:2.036   1st Qu.:115.5  
 Median :0.01230   Median :0.5245   Median :0.02430   Median :2.203   Median :121.0  
 Mean   :0.01316   Mean   :0.5411   Mean   :0.02454   Mean   :2.299   Mean   :129.4  
 3rd Qu.:0.01403   3rd Qu.:0.5718   3rd Qu.:0.02598   3rd Qu.:2.432   3rd Qu.:138.0  
 Max.   :0.02227   Max.   :0.5862   Max.   :0.04342   Max.   :3.030   Max.   :219.0  

mining info:

inspect(rule)

data(AdultUCI)
class(AdultUCI)

[1] "data.frame"

AdultUCI <- lapply(AdultUCI, function(x) {as.factor(x)})
str(AdultUCI)

List of 15
 $ age           : Factor w/ 74 levels "17","18","19",..: 23 34 22 37 12 21 33 36 15 26 ...
 $ workclass     : Factor w/ 8 levels "Federal-gov",..: 7 6 4 4 4 4 4 6 4 4 ...
 $ fnlwgt        : Factor w/ 28523 levels "12285","13492",..: 3462 3789 18343 19996 25406 23150 11188 17736 1680 11094 ...
 $ education     : Ord.factor w/ 16 levels "Preschool"<"1st-4th"<..: 14 14 9 7 14 15 5 9 15 14 ...
 $ education-num : Factor w/ 16 levels "1","2","3","4",..: 13 13 9 7 13 14 5 9 14 13 ...
 $ marital-status: Factor w/ 7 levels "Divorced","Married-AF-spouse",..: 5 3 1 3 3 3 4 3 5 3 ...
 $ occupation    : Factor w/ 14 levels "Adm-clerical",..: 1 4 6 6 10 4 8 4 10 4 ...
 $ relationship  : Factor w/ 6 levels "Husband","Not-in-family",..: 2 1 2 1 6 6 2 1 2 1 ...
 $ race          : Factor w/ 5 levels "Amer-Indian-Eskimo",..: 5 5 5 3 3 5 3 5 5 5 ...
 $ sex           : Factor w/ 2 levels "Female","Male": 2 2 2 2 1 1 1 2 1 2 ...
 $ capital-gain  : Factor w/ 123 levels "0","114","401",..: 28 1 1 1 1 1 1 1 110 82 ...
 $ capital-loss  : Factor w/ 99 levels "0","155","213",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ hours-per-week: Factor w/ 96 levels "1","2","3","4",..: 40 13 40 40 40 40 16 45 50 40 ...
 $ native-country: Factor w/ 41 levels "Cambodia","Canada",..: 39 39 39 39 5 39 23 39 39 39 ...
 $ income        : Ord.factor w/ 2 levels "small"<"large": 1 1 1 1 1 1 1 2 2 2 ...

transaction <- as(AdultUCI,"transactions")

Warning in asMethod(object) : removing duplicated items in transactions
Error in validObject(.Object) : 
  invalid class “ngCMatrix” object: row indices are not sorted within columns

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